Creating A Customized Study Plan

**In this segment, we’re guided through how to create an effective, targeted prep plan for ML interviews. Preparation should be intentional, role-specific, and company-informed.**

- **Create a tailored study plan** based on your target role. Spend 1–2 weeks on behavioral prep, overlap topics wisely, and prioritize deliberate, focused practice.
- **Do company-specific preparation** by researching their tech stack, ML frameworks, and use cases. Read blogs, papers, and talk to current employees to understand their challenges and how they approach ML.
- **For product-focused ML roles**, focus on business impact, case studies, model selection trade-offs, and deep feature engineering—interviewers value end-to-end thinkers.
- **For infrastructure-focused ML roles**, focus on scalable systems, distributed training, MLOps best practices, feature stores, and real-world reliability scenarios like traffic spikes.
- **For research-oriented ML roles**, go deep into recent papers, experimental design, evaluation strategies, and be ready to explain your own contributions with clarity—even to non-experts.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Amazon Behavioral Interview [Part 29] - Building Trust With Stakeholders (Answer)

This video segment focuses on how to build trust relationships with colleagues and stakeholders.

- **Key Strategies:**

  - **Proactive Relationship Building:**
    - The interviewee emphasizes the importance of proactive relationship building by reaching out to stakeholders early on, even before needing to rely on them. This includes conducting preliminary research, scheduling introductory meetings, and taking the time to understand their individual goals and priorities.
  - **Demonstrating Integrity and Consistency:**
    - The interviewee highlights the importance of acting with integrity, following through on commitments, and being transparent about their goals and priorities. This builds trust and credibility with colleagues.
  - **Assuming Trust:**
    - While acknowledging the importance of earning trust, the interviewee believes in starting with a foundation of trust and assuming positive intent from others.
  - **Open and Honest Communication:**
    - The interviewee emphasizes the importance of open and honest communication, including being transparent about priorities and setting clear expectations.

- **Practical Tips:**

  - **Leverage tools like "The First 90 Days" to effectively identify and map stakeholders.**
  - **Conduct introductory meetings with key stakeholders to build rapport and understand their perspectives.**
  - **Focus on building relationships based on mutual respect and trust.**
  - **Prioritize consistency and follow-through on commitments.**
  - **Communicate openly and honestly about goals and priorities.**

This approach to building trust relationships emphasizes proactiveness, integrity, open communication, and a focus on understanding and meeting the needs of stakeholders.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Ask Me Anything (AMA) with a Senior Eng Director - Sanjay Siddhanti

[**Sanjay Siddhanti**](https://www.linkedin.com/in/sanjaysiddhanti/) is a Senior Director of Engineering at AKASA. After graduating from Stanford, he previously held engineering roles at Counsyl, Karius, and Flatiron Health.

Sanjay's background:

I'm the Senior Director of Engineering at AKASA. I joined AKASA in 2019 as one of the first employees when we were a seed-stage startup. I originally joined AKASA as an individual contributor, and quickly switched over to management. I built much of the company's early technology as an IC and later as a tech lead / manager. I also started and managed multiple engineering teams at the company, including Platform Engineering, now an org with 20 engineers.

Happy to answer questions about:

- How engineering leaders think about the role of Senior, Staff, and Principal ICs.
- How and why to transition from IC to management
- The differences between an Engineering Manager vs Engineering Director
- How to hire and retain great talent
- How to succeed in a startup environment.

How Managers Hire Great Engineers [Part 15] - Initial Contact

You only get one first impression, so you must make that initial outreach message count. From this lesson, you will:

- Discover how to craft an engaging subject line and personalize the content to stand out from typical recruiter communications.
- Understand the importance of keeping the email concise, with a clear, brief point that invites the recipient to engage further.
- Learn optimal timing for sending emails to maximize response rates, avoiding busy times like early Monday or late Friday.

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

Underrated Career Goals

We highlight the qualities engineers consistently underestimate and why these matter far more than most people realize when choosing a company or team.

Here are the core points from the lesson:

- We emphasize that the most underrated factor by far is a truly supportive team: a strong manager, teammates who challenge and care about us, and an environment where we grow—something incredibly rare, worth cherishing, and often more valuable than joining a prestigious brand.
- We explain that growth opportunities don’t come automatically from prestige; even at places like Meta or Google, you can land on a team with poor scope or weak culture, and real growth often requires intentional team selection and proactive effort.
- We stress that stability and sustainable work-life balance are often undervalued by early-career engineers, who assume they can power through long hours, even though sustained 60-hour weeks degrade learning, productivity, and mental health—and that the best creators historically do only a few hours of deep work daily.

How You Can Supercharge Your Team - Discovering Synergies

Having The Meeting

In this lesson, we walk through exactly how to run a high-impact 1:1 with our EM by using the time intentionally to build trust, surface wins, work through challenges, and create momentum through clear action items.

- We start each meeting with 2–5 minutes of personal check-in to strengthen rapport, since life and work affect each other deeply.
- We share weekly wins to stay visible and ensure our contributions are recognized—while also making future performance review writing much easier.
- We move through our pre-seeded agenda topics (e.g., promotion planning, feedback digestion, team conflicts), which flow smoothly because we prepared them ahead of time.
- We close with clear action items for both sides—then begin the next week’s 1:1 by reviewing whether the previous actions were completed.
- We reinforce that framing wins with a “so what?” (impact or next step) and writing notes live during the meeting both strengthen trust and make the manager feel valued.

Understanding The Role

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This part of the course explains why becoming an engineering manager (EM) is not just a promotion—it’s a **career pivot**. Understanding the differences in scope and responsibilities between individual contributors and managers is essential to succeed in EM interviews and in the role itself.

- **Role Shift:** Transitioning from engineer to EM means moving from primarily technical tasks to handling broader responsibilities involving people and strategy.
- **Scope vs. Focus:** Senior engineers focus on deep technical problems, while EMs begin to balance both technical and organizational concerns; directors and VPs go even further toward people/strategy.
- **Career Tracks Diverge:** Staying on the IC track keeps you focused on technology, but switching to EM roles sets you on a path toward executive leadership (e.g., director, VP).
- **Lack of Preparation:** Most engineers aren’t trained to manage people or lead strategically, which causes difficulties when transitioning into EM roles.
- **Wrong Reasons:** Some pursue management purely to climb the ladder, which can result in disengaged or ineffective leaders who use their teams as stepping stones.

System Design Example: TikTok Recommendation System

Here’s a summary of the **infrastructure-focused ML system design** walkthrough. We are building a robust, scalable recommendation platform to support multiple product teams, focusing on infrastructure needs such as latency, reliability, and modularity.

- **We design for scalability and flexibility**, supporting 10M+ daily predictions across various surfaces (e.g., homepage, search, email), with &lt;50ms latency for real-time use cases and batch support for offline use.
- **We architect the system** with modular components: batch and streaming feature pipelines, a centralized feature store, a model training platform, a model registry, and scalable inference servers, all integrated with monitoring and alerting.
- **We implement tiered feature computation** (long-term, medium-term, real-time) using tools like Spark and Flink, and store user, item, and contextual features consistently to power accurate and fresh predictions.
- **We support multiple modeling strategies**, including two-tower models, matrix factorization, and DNNs, and use a two-stage architecture with candidate generation followed by precise ranking.
- **We deploy models reliably** using CI/CD pipelines, blue-green deployments, and shadow testing, and maintain performance through A/B testing, autoscaling servers, inference optimizations, and drift detection.

This approach ensures we build ML systems that are production-ready, highly available, and adaptable to the evolving needs of diverse teams.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

ML Algorithm Example: K-means

In this segment, we walk through a high-level structure for implementing **k-means clustering from scratch**, focusing on clarity, modularity, and real-time reasoning. The emphasis is not on memorizing code, but on understanding the logic and being able to build it confidently.

- We start by defining an `__init__` method to store **core parameters** like the number of clusters (k) and max iterations.
- We **initialize centroids** by randomly selecting k data points; interviewers may also ask about smarter strategies like **k-means++**.
- In the **assign_clusters** step, we calculate distances from each point to every centroid and assign points to the nearest one.
- The **update_centroids** method recomputes each centroid as the mean of its assigned points; optionally, we may include a **loss function** to track clustering quality.
- The **fit** method loops through initialization, assignment, and updates until convergence, while **predict** assigns clusters to new data after training.
- Most importantly, we aim for **clean, modular, and well-structured code**, and narrate our logic during the interview to demonstrate deep understanding—not just syntax.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Notebook Interview Example: Ad Click Prediction

In this final part of the section, we bring all the core concepts together through a realistic, end-to-end ML interview example. The walkthrough demonstrates how to apply best practices across the entire notebook round—from EDA to model evaluation—with clarity, intention, and real-world relevance.

- In **EDA**, we go beyond surface checks by exploring unique value counts, visualizing target distribution, spotting class imbalance, and identifying trends like CTR by device type—ensuring every insight informs downstream modeling.
- In **feature engineering**, we thoughtfully create new features (e.g., age buckets, time-of-day flags), handle multicollinearity, and tailor transformations based on domain intuition, not just standard recipes.
- During **model training**, we justify our choice of random forest for its robustness and interpretability, apply cross-validation and stratified splits, and run grid search for tuning—demonstrating intention behind every step.
- In **evaluation**, we use metrics that align with the problem (precision, recall, F1, AUC-ROC, confusion matrix) and interpret results in context, showing we understand the real-world stakes of false positives and negatives.
- For **follow-up questions**, we proactively discuss improvements (e.g., deeper models, richer features), production monitoring (CTR by segment, drift detection), and handling class imbalance—proving we think like end-to-end ML practitioners, not just coders.

This example illustrates how to move from “checking boxes” to demonstrating thoughtful, production-aware problem-solving under real interview conditions.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Amazon Behavioral Interview [Part 28] - Recovering From A Bad Decision (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they made a bad decision.

- **Strengths:**

  - **Owned the Mistake:** The interviewee openly acknowledged the mistake and took responsibility for the negative consequences.
  - **Demonstrated Self-Awareness:** They identified the root cause of the mistake – focusing solely on short-term revenue gains while neglecting long-term customer value and potential.
  - **Highlighted Corrective Actions:** The interviewee described the steps taken to rectify the situation, such as revising the incentive structure, improving call center response times, and implementing measures to address long wait times.
  - **Learned from the Experience:** The interviewee outlined the key learnings from this experience, including the importance of considering long-term customer value and gathering input from stakeholders before implementing significant changes.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have further quantified the impact of the mistake and the subsequent corrective actions. For example, they could have provided specific data on the improvement in customer satisfaction scores or the increase in customer retention rates after implementing the changes.
  - **Emphasize Positive Outcomes:** The interviewee could have further emphasized the positive outcomes of the project, such as the improved focus on customer lifetime value and the development of a more robust customer retention strategy.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions and learnings to relevant Amazon Leadership Principles, such as "Customer Obsession," "Learn and Be Curious," and "Ownership."

- **Overall:**

  - The interviewee effectively demonstrated their ability to acknowledge and learn from mistakes, take responsibility for their actions, and implement corrective measures.
  - By incorporating the suggested improvements, the interviewee can further enhance their response and increase their chances of success in the interview.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 26] - Working Outside Of Your Domain (Evaluation)

This video segment analyzes an individual's response to a behavioral interview question about taking on a project outside of their core responsibilities.

- **Strengths:**

  - **Identified a Problem:** The interviewee effectively identified a critical issue: low employee engagement scores regarding promotion fairness.
  - **Took Initiative:** They proactively sought to address this issue by developing and implementing a new feedback system ("Perfolution").
  - **Demonstrated Collaboration:** The interviewee successfully collaborated with HR to implement the system and gather data.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have more clearly quantified the impact of the "Perfolution" system. Instead of simply stating that scores "went up," they could have provided specific data points, such as the percentage increase in employee satisfaction scores related to promotion fairness.
  - **Focus on Leadership:** The interviewee could have further emphasized their leadership role in driving the project, such as:
    - Motivating their team to participate in the feedback process.
    - Overcoming resistance or challenges encountered during implementation.
    - Communicating the results of the project to key stakeholders and securing buy-in for necessary changes.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions to relevant Amazon Leadership Principles, such as "Ownership," "Customer Obsession," and "Bias for Action."

- **Overall:**

  - While the interviewee demonstrated initiative and problem-solving skills, the response could be strengthened by:
    - Providing more specific and quantifiable data on the impact of the project.
    - Further emphasizing their leadership role and the challenges they overcame.
    - Explicitly connecting their actions to relevant Amazon Leadership Principles.

This analysis provides valuable insights into how to effectively communicate accomplishments and demonstrate leadership qualities in a job interview context.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Adam’s Advice to His Younger Self

In this final lesson, **Adam Ernst**, Software Engineer at **Meta**, reflects on his career journey and the advice he would give to aspiring engineers. Looking back, Adam believes he would not change the path he took because it aligned with his genuine interests and strengths. He emphasizes that long-term success comes from enjoying the work itself—especially the challenge of solving difficult problems—rather than chasing titles or following trends. By pursuing work that naturally fuels curiosity and motivation, engineers are more likely to sustain high performance and meaningful career growth.

- **Follow Work You Genuinely Enjoy:** Adam believes the best career decisions come from pursuing work that you find intrinsically rewarding. Passion creates the motivation needed to continually learn, improve, and produce high-quality work over the long term.
- **Love Solving Problems, Not Just Writing Code:** While not every task is enjoyable, Adam finds fulfillment in tackling challenging engineering problems. A genuine curiosity for problem-solving is what keeps difficult work engaging and ultimately leads to sustained success.
- **There's No Perfect Career Blueprint:** Rather than trying to optimize every career decision in hindsight, Adam's experience suggests that success comes from consistently following opportunities that align with your interests and strengths. When what you enjoy also meets the needs of the industry, you're in an ideal position to thrive.

Original podcast link: [**https://www.youtube.com/watch?v=YA_OYJF3Mmw**](https://www.youtube.com/watch?v=YA_OYJF3Mmw)

Be Upfront About Your Goals

In this lesson, we emphasize the importance of sharing our performance goals openly with our EM so they can effectively advocate for us during performance reviews, which are human-driven, subjective, and highly dependent on manager alignment.

- We highlight that performance ratings are not generated by an objective system (i.e. they’re decided by people), so managers need clear insight into what rating we’re aiming for to advocate properly.
- We stress that “no negative feedback” does *not* mean we’re on track for a good rating or promotion; assuming this is one of the most common and costly mistakes ICs make.
- We reinforce that 1:1s are the central place to share our target rating or promotion timeline, whether we’re aiming for “meets,” “exceeds,” or a promotion, so we can then build a plan with our EM.
- We note that even if our goal is simply to maintain solid performance (e.g., a 3/5), we should still say so explicitly to avoid slipping into a low rating unexpectedly.
- We share a story illustrating how unchecked assumptions about performance can lead to disappointment—and how early, honest conversations could provide clarity and opportunities to improve.

Why Coding Matters In ML

In this new section on **coding fundamentals**, we explore why strong programming skills are essential for machine learning engineers. While ML involves data and models, it’s also deeply rooted in real-world software engineering.

- We clarify a common myth: **ML engineers are still engineers**, and are expected to meet the same coding standards as traditional software engineers.
- Our **software engineering background is an asset**—if applied correctly, it can set us apart from more research-focused ML candidates.
- **Coding matters in ML** because models must be integrated into production systems, maintained, and optimized for performance and scalability.
- We’re reminded to consider **algorithmic complexity**, especially when building pipelines or serving models at scale.
- The key takeaway: **don’t underestimate LeetCode-style coding prep**. Many ML interviews, especially at top tech companies, still include traditional algorithm and data structure questions. Strong coding skills are non-negotiable.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Become An Engineer Influencer [Part 19] - What If Nothing Works?

Partner, Not A Boss

In this lesson, we talk about shifting the mindset from seeing an engineering manager as an authority figure to treating them as a partner whose incentives align closely with ours, unlocking far more growth and support.

- We emphasize that while EMs have real power (performance reviews, promotions, PIPs), relating to them through fear undermines trust, collaboration, and growth.
- We highlight that both we and our EM share the same fundamental goal: maximizing our impact, because our increased impact directly elevates their team impact.
- We explain that when we grow, deliver more, and navigate the org effectively, our manager looks better—so supporting our development is in their best interest.
- We encourage adopting a peer-like, partnership mentality rather than a hierarchical one, which creates more openness, trust, and meaningful dialogue.
- We reinforce the idea that adding value to our manager—through communication, initiative, and impact—encourages them to reciprocate with resources, advocacy, and opportunities.

Become An Engineer Influencer [Part 24] - Consulting

Maximize Your Equity And Benefits In Tech [Part 22] - Healthcare And Miscellaneous Benefits

From picking the right medical plan to using hidden financial perks, there’s a lot you can do to stretch your benefits beyond equity. Here are the core points from the lesson:

- PPO, EPO, and HMO plans vary in flexibility and cost, with PPOs offering the most freedom and HMOs the lowest cost but most restrictions
- Understanding plan terms like premiums, deductibles, copays, and coinsurance helps you estimate total medical costs more accurately
- HSAs offer exceptional tax advantages and long-term growth potential, especially if you invest the funds and save receipts for future reimbursement
- FSAs are employer-based, use-it-or-lose-it accounts that are best used only when you know you’ll spend the funds during the year
- Tech workers may also have access to other valuable benefits like legal services, fertility support, wellness stipends, and insurance—but employer-provided life and disability coverage may not be enough if you truly need it

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.